IEMOCAP, an audio-visual affect data set, is used to train and validate the models [1]}. The data set comprises of annotations representing both the categorical and dimensional emotional model [2]}. The models are trained with data from the emotional categories neutral (N), sad (S), happy (H), angry (A). We use the extended Geneva minimalistic acoustic parameter set (eGeMAPS) [3]}, specifically the functionals of lower-level features. Each speech sample yields a feature vector comprising of 88 features and we use the OpenSmile toolkit to extract the features [4]}. To study how the latent representations are transferred between corpora and languages, we test classification accuracy over the following transfer datasets :
